An ACRL white paper from 2012 reported that, at that time, only a small number of academic libraries in the United States and Canada offered research data services (RDS), but many were planning to do so within the next two years (Tenopir, Birch, and Allard, 2012). By 2013, 74% of the Association of Research Libraries (ARL) survey respondents offered RDS and an additional 23% were planning to do so (Fearon, Gunia, Pralle, Lake, and Sallans, 2013). The academic libraries recognize that the landscape of services changes quickly and that they need to support the changing needs of research and instruction. In their efforts to implement RDS, libraries often respond to pressures originating outside the library, such as national or funder mandates for data management planning and data sharing. To provide effective support for researchers and instructors, though, libraries must be proactive and develop new services that look forward and yet accommodate the existing human, technological, and intellectual capital accumulated over the decades. Setting the stage for data curation in libraries means to create visionary approaches that supersede institutional differences while still accommodating diversity in implementation. How do academic libraries work towards that? This chapter will combine an historical overview of RDS thinking and implementations based on the existing literature with an empirical analysis of ARL libraries’ current RDS goals and activities. The latter is based on the study we conducted in 2015 that included a content analysis of North American research library web pages and interviews of library leaders and administrators of ARL libraries. Using historical and our own data, we will synthesize the current state of RDS implementation across ARL libraries. Further, we will examine the models of research data management maturity (see, for example, Qin, Crowston and Flynn, 2014) and discuss how such models compare to our own three-level classification of services and activities offered at libraries - basic, intermediate, and advanced. Our analysis will conclude with a set of recommendations for next steps, i.e., actions and resources that a library might consider to expand their RDS to the next maturity level. References Fearon, D. Jr., Gunia, B., Pralle, B.E., Lake, S., Sallans, A.L. (2013). Research data management services. (ARL Spec Kit 334). Washington, D.C.: ARL. Retrieved from: http://publications.arl.org/Research-Data-Management-Services-SPEC-Kit-334/ Tenopir, C., Birch, B., & Allard, S. (2012). Academic libraries and research data services: Current practices and plans for the future. ACRL. Retrieved from http://www.ala.org/acrl/sites/ala.org.acrl/files/content/publications/whitepapers/Tenopir_Birch_Allard.pdf Qin, J., Crowston, K., & Flynn, C. (2014). 1.1 Commitment to Perform. A Capability Maturity Model for Research Data Management. wiki. Retrieved http://rdm.ischool.syr.edu/xwiki/bin/view/CMM+for+RDM/WebHome

Universities are increasing efforts to assist faculty, students, and community partners to bring inventions to market. As state funding for higher education continues to shrink, universities are looking for new revenue streams to support teaching, research, and learning. Campus commercialization efforts are a strategy to promote economic development opportunities that benefit the community and society at large. Commercialization efforts on campus include technology transfer offices, incubator parks, and joint ventures between the university, investors, and corporate entities. Academic libraries have the opportunity to partner with campus technology transfer efforts and expand librarian roles and responsibilities to support commercialization, innovation,and entrepreneurship on campus